opencv 3.1 VS 2015 thresholding algorithm

Image Binarization/Thresholding
OpenCV implementation -2016-
Introduction
“thresholding" is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image applications. The main purpose of thresholding are separating objects from background, decreasing the capacity of data consequently increases speed
The thresholding process is a critical step during the segmentation phase. It is also an important part for image processing and pattern recognition. Implementing thresholding different reasons such as increase speed and use small storage space. Therefore, the segmentation accuracy can be increased if the estimated thresholding value is achievable.
Background
single thresholding
Multilevel Threshold
thresholding
One of the important parts of image segmentation
Separate foreground and background in order to have a better object recognition
Decreasing the capacity of data storage by using thresholding leads to increase speed
Causes to remove the noise
Challenges of Thresholding
Accuracy
Base on type of application
Speed
Find suitable threshold value
Adaptive on area that work
Separate object from image properly
Work by lack of resource
PSNR
Pseudo code of our algorithm
The Proposed Threshold Algorithm.
DIBCO
Publication
Peak Signal-to-noise Ratio Based On Threshold Method For Image Segmentation
Journal of Theoretical and Applied Information Technology
Adaptive Image Thresholding Based on the Peak Signal-to-noise Ratio
Research Journal of Applied Sciences, Engineering and Technology
Comparison Single Thresholding Method for Image Segmentation on Hand Written Images
Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate recognition system
Multi-threshold approach for license plate recognition system
An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis
OpenCV implementation -2016-
Using OpenCV 3.1 and Visual studio 2015
This is the most common and the simplest type of thresholding.
ADAPTIVE_THRESH_GAUSSIAN_C
- filter image from grayscale to black and white
adaptiveThreshold -imgGrayscale, - input image
255, - make pixels that pass the threshold full white
ADAPTIVE_THRESH_GAUSSIAN_C, - use Gaussian rather than mean, seems to give better results
THRESH_BINARY_INV, - invert so foreground will be white, background will be black
11, - size of a pixel neighborhood used to calculate threshold value
2- - constant subtracted from the mean or weighted mean
Latest thresholding method -PirahanSiah method 2016-
Algorithm of PirahanSiah method -2016-
Objectives
Developing a new thresholding algorithm
This work proposed an algorithm that applies peak signal noise ratio method as one of the indicator to segment the images. This algorithm will then be compared with other existing algorithms and the results will be analyzed.
performing a comparison with other thresholding methods
scopes
The scopes of the research are as follows:
Database
Standard images
Hand written images -DIBCO -
Printed images -DIBCO-
Configurations
Offline processing
Software
VC++
Package Libraries
OpenCV -Open Source Computer Vision-
Platform Windows

[shadisaleh]

Hallo is also possible to get the slides :) thanks